Mammalian hosts, from inbred mouse strains to humans, display a wide range of phenotypic responses to various bacterial infections. One of the pathogens eliciting such differential responses in mice is Listena monocytogenes, a Gram-positive bacterium which is a common source of food poisoning. Previous studies have established that differences in immune response are often genetically controlled. Differences in immune response can be characterized using a variety of readouts, from gene expression changes to disease outcome. Currently, gene expression data is the most comprehensive way to describe a biological system. In addition, gene expression values can be used directly in genetic quantitative trait mapping experiments. Therefore, by monitoring differential transcriptional responses to infection, we can identify and characterize molecules critical for control of infection. To achieve this goal we will perform genome-wide characterization of gene expression profiles in differentially susceptible inbred mouse strains following infection with Listeria monocytogenes. To understand how changes in gene expression affect immune function, we have to differentiate between primary and secondary differences. Primary expression differences are caused by genetic polymorphisms at the gene locus itself, which directly affect the level of the gene transcript. Accordingly, secondary differences are those which reflect upstream functional or expression differences. We will use a novel Recombinant Inbred expression mapping approach to differentiate between primary and secondary changes in gene expression in mouse strains differentially susceptible to Listeria monocytogenes infection. Our pilot study will identify molecules differentially regulated in response to infection. Furthermore, it will allow us to refine our methodology for a future comprehensive study of changes in gene expression in differentially susceptible hosts over the course of the infectious disease. Identification of primary gene expression changes will lead to identification of genetic polymorphisms controlling gene expression. In addition, these primary expression differences will serve as starting points for building a multilayer map of transcriptional responses to infection. We hope to use this map to identify novel therapeutic targets for treatment of infectious diseases.